查詢教學大綱與進度

教學大綱與進度

課程基本資料：

學年期課號課程名稱階段學分時數修教師班級人撤備註

114-1 351867 機器學習 1 3.0 3 ★ 謝東儒資工所 90 2 資工四、資工所合開

學年期	課號	課程名稱	階段	學分	時數	修	教師	班級	人	撤	備註
114-1	351867	機器學習	1	3.0	3	★	謝東儒	資工所	90	2	資工四、資工所合開

教學大綱與進度：

教師姓名謝東儒　教師諮商時間(Office Hours)

Email tjhsieh@ntut.edu.tw

最後更新時間 2026-06-08 13:49:51

課程大綱本課程以深入淺出之方式，介紹與機器學習之相關技術及演算法，目標是修習本課程之學生具有閱讀機器學習相關文獻之基本知識，並能從事相關之實務或電腦模擬實驗之工作，因此本課程著重在廣泛介紹各類之演算法之應用，而非深究少數演算法之深奧數學原理．本課程涵蓋之演算法，包含：最佳化基本知識，隨機最佳化演算法，隨機搜尋，決策樹，隨機森林，類神經網路及深度學習相關演算法，基因演算法，貝氏定理及應用，高斯混合模型，隱藏式馬可夫鍊(及訓練演算法)，支援向量機，線性及邏輯回歸，分群方法，降維方法，提升方法，決策融合技巧，及增強式學習等。 This course covers most of the techniques and algorithms related to the current trend in the machine learning field. The goal of the course is that students complete this course can read and understand the literature in the machine learning field, and can conduct computer simulations for experiments or apply the techniques to practical problems. Therefore, the course intends to cover a broad range of algorithms with emphasis on applications, rather than to cover only a few algorithms with extensive mathematical treatments. The algorithms covered in this course include: foundations of optimizations, random search, decision tree, random decision forests, neural networks and deep learning, genetic algorithm, Bayes' theorem and its applications, Gauss mixture model, hidden Markov model, support vector machine, linear and logistic regressions, clustering algorithms, dimension reduction techniques, boosting methods, decision level fusion techniques, and reinforcement learning.

課程進度 Week 1: Class Announcement Week 2: Introduction to Machine Learning Week 3: Basics of Supervised Learning (Classification and Regression) Week 4: VC dimension, Bayesian Decision Theory, Naive Bayes Classifiers Week 4: Dimension Reduction Techniques: PCA, FA, LDA, ICA Week 5: Unsupervised learning: Clustering Algorithm (k-means) Week 6: Decision Trees, Bagging Algorithm (Random Forest) Week 7: Basics of optimization Week 8: Basics of Optimization Week 9: Gradient Search Week 10: Feedforward Neural Networks Week 11: Multi-layer Perceptrons, Back Propagation, Regularization Methods Week 12: Deep Learning and Convolutional Neural Networks Week 13: Deep Neural Networks: YOLO, Autoencoder, LSTM Week 14: Bayesian Networks, Confidence of Classification, Self-supervised learning Week 15: SVM, Adaboost, Ensemble Learning Week 16: Final exam Week 17: Presentation Week 18: Presentation

評量方式與標準 Homework Projects 60% Midterm 20% Final 20 %

使用教材、參考書目或其他 【遵守智慧財產權觀念，請使用正版教科書，不得使用非法影印教科書】

使用外文原文書：否
Course PPT files are available for students.

課程諮詢管道 Office: Tech building 1533
Email: tjhsieh@ntut.edu.tw

延伸教學與資源 ● 數位教材資源應用 (Application of Digital Resources)
採用影音多媒體或具開放授權之資源（如OCW、開放教科書）作為主要或輔助教材。
Adopts multimedia or open-licensed resources (e.g., OCW, Open Textbooks) as primary or supplementary teaching materials.

課程對應SDGs指標 SDG4：優質教育（Quality Education）
SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure）

課程是否導入AI ● 深入探討AI的原理與應用（Explore the principles and applications of AI in depth）

備註　

教師姓名	謝東儒　教師諮商時間(Office Hours)
Email	tjhsieh@ntut.edu.tw
最後更新時間	2026-06-08 13:49:51
課程大綱	本課程以深入淺出之方式，介紹與機器學習之相關技術及演算法，目標是修習本課程之學生具有閱讀機器學習相關文獻之基本知識，並能從事相關之實務或電腦模擬實驗之工作，因此本課程著重在廣泛介紹各類之演算法之應用，而非深究少數演算法之深奧數學原理．本課程涵蓋之演算法，包含：最佳化基本知識，隨機最佳化演算法，隨機搜尋，決策樹，隨機森林，類神經網路及深度學習相關演算法，基因演算法，貝氏定理及應用，高斯混合模型，隱藏式馬可夫鍊(及訓練演算法)，支援向量機，線性及邏輯回歸，分群方法，降維方法，提升方法，決策融合技巧，及增強式學習等。 This course covers most of the techniques and algorithms related to the current trend in the machine learning field. The goal of the course is that students complete this course can read and understand the literature in the machine learning field, and can conduct computer simulations for experiments or apply the techniques to practical problems. Therefore, the course intends to cover a broad range of algorithms with emphasis on applications, rather than to cover only a few algorithms with extensive mathematical treatments. The algorithms covered in this course include: foundations of optimizations, random search, decision tree, random decision forests, neural networks and deep learning, genetic algorithm, Bayes' theorem and its applications, Gauss mixture model, hidden Markov model, support vector machine, linear and logistic regressions, clustering algorithms, dimension reduction techniques, boosting methods, decision level fusion techniques, and reinforcement learning.
課程進度	Week 1: Class Announcement Week 2: Introduction to Machine Learning Week 3: Basics of Supervised Learning (Classification and Regression) Week 4: VC dimension, Bayesian Decision Theory, Naive Bayes Classifiers Week 4: Dimension Reduction Techniques: PCA, FA, LDA, ICA Week 5: Unsupervised learning: Clustering Algorithm (k-means) Week 6: Decision Trees, Bagging Algorithm (Random Forest) Week 7: Basics of optimization Week 8: Basics of Optimization Week 9: Gradient Search Week 10: Feedforward Neural Networks Week 11: Multi-layer Perceptrons, Back Propagation, Regularization Methods Week 12: Deep Learning and Convolutional Neural Networks Week 13: Deep Neural Networks: YOLO, Autoencoder, LSTM Week 14: Bayesian Networks, Confidence of Classification, Self-supervised learning Week 15: SVM, Adaboost, Ensemble Learning Week 16: Final exam Week 17: Presentation Week 18: Presentation
評量方式與標準	Homework Projects 60% Midterm 20% Final 20 %
使用教材、參考書目或其他	【遵守智慧財產權觀念，請使用正版教科書，不得使用非法影印教科書】使用外文原文書：否 Course PPT files are available for students.
課程諮詢管道	Office: Tech building 1533 Email: tjhsieh@ntut.edu.tw
延伸教學與資源	● 數位教材資源應用 (Application of Digital Resources) 採用影音多媒體或具開放授權之資源（如OCW、開放教科書）作為主要或輔助教材。 Adopts multimedia or open-licensed resources (e.g., OCW, Open Textbooks) as primary or supplementary teaching materials.
課程對應SDGs指標	SDG4：優質教育（Quality Education） SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure）
課程是否導入AI	● 深入探討AI的原理與應用（Explore the principles and applications of AI in depth）
備註